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Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes called the seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size.
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A. V. Sathanur, M. Halappanavar, Y. Shi, and Y. Sagduyu, “Exploring the role of intrinsic nodal activation on the spread of influence in complex networks,” in Social Network Based Big Data Analysis and Applications . Springer, 2018, pp. 123–142
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M. Minutoli, M. Halappanavar, A. Kalyanaraman, A. Sathanur, R. Mcclure, and J. McDermott, “Fast and scalable implementations of influence maximization algorithms,” in 2019 IEEE International Conference on Cluster Computing (CLUSTER) , 2019, pp. 1–12
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